SC&I Courses

Required each semester a student is in course work. Forum for the presentation of research and professional activities by guest speakers, faculty, and students.

The pro-seminar addresses nature of communication, information, and media processes and their role in individual, social, and institutional behavior. Particular emphasis will be on the conceptual linkages between communication, information, and media processes, as well as theory and meta-theory. Panels will alternate between interdisciplinary and area-specific topics featuring CILS program faculty as speakers. Pro-seminar will include topics in professional development, academic integrity, responsible and ethical conduct of research, intellectual property. Students will pass the Human Subjects Certification Program as part of the course.

Qualitative approaches for examining communication and information processes, including information definition, acquisition, evaluation and use.

Facets of research, problem areas, research techniques and experiments. Each student develops a research project relating to a chosen topic.

About the statistics competency prerequisite
The program expects students entering the program to have achieved master’s level competency in statistics. This is a prerequisite for enrollment in Quantitative Research Methods (16:194:604), a program core methods course option for all students. Competency in statistics will be assessed by the instructor for 604 in the semester prior to enrollment. Students who have not successfully completed graduate level coursework in statistics, or feel unsure about their statistical competency, are strongly encouraged to enroll in a master’s level statistics course as soon as possible. Credits earned in elementary master’s level statistics do not count toward the program’s course work credit requirements. Possible courses at Rutgers include (but are not restricted to) 17:610:511 - Research Methods (in the Master of Information program) and 16:960:532: Statistical Methods in Education (in the Graduate School of Education).

Assessment for statistics competency
Faculty assessing preparation for 16:194:604 will generally ask students to provide the following evidence of competency, among possible others:

Levels of Measurement

Provide brief definitions and examples of nominal, ordinal, interval, and ratio levels of measurement. Possible elaborations include increasing statistical sensitivity, tests of association for nominal and interval/ratio data, and issues in making continuous measures discrete.

Measures of Central Tendency (mean, median, mode)

Provide brief definitions of each, know with what types of data each would be used, what it means when they are all similar, what it signifies when they differ, and why these measures are important descriptors.

Measures of Variance

Define standard deviation, evaluate any frequency distribution in terms of its standard deviations, compare the standard deviation to standard error, and determine the range and variance of a sample.

Variables

Identify independent/predictor, confounding, moderator/intervening, and dependent/criterion variables. Understand appropriate use of the different terms.

Sampling

Understand the difference between probability and nonprobability sampling, samples versus populations, parametric versus nonparametric distributions, types of sampling, assumptions of normal distributions, other types of distributions (e.g., poisson, t, chi-square, etc.)

Error

Understand Type I and Type II errors, sampling and measurement error.

Tests of Association

Understand cross-tabulations and chi-square analyses, t -tests, analyses of variance, and different kinds of correlations.

Significance

Be familiar with p values, degrees of freedom, sample size, relationship of p values to alpha, choosing significance levels, and the relationships among statistical power, significance levels, generality/generalizability, and sample size. Be able to look up critical values on t, chi-square, or normal distribution tables.

Z-scores

Define standard normal curve, standard scores, know formula and appropriate uses

This course introduces students to a variety of interpretive research methods employed to study the media and communication. Looking at the historical development and epistemological foundations of such methods, we will discuss what constitutes interpretive methodologies and how they differ from other forms of qualitative research.  We will explore a range of commonly employed methods such as ethnography, historical methods, the analysis of visual texts, semiotics, ideological criticism, and legal interpretive methods. We will pay particular attention to ethical issues and pragmatic techniques as we read essays by leading scholars who employ interpretive methods in their research.  Students will not only become familiar with interpretive research traditions and see how they are applied to real life media phenomena, but will also learn how to distinguish well done from sloppy research.

Taken by arrangement with faculty member, usually toward the end of coursework, this requirement is met by a student registering for this course with a member of the program faculty who has agreed to supervise the student’s participation in a research project of interest. Students are expected to contact faculty directly with practicum projects/ideas. The expected outcome of the Research Practicum is a paper (single or co-authored) submitted to a recognized conference or refereed journal. The results of the Research Practicum will be presented by the student at a Ph.D. Poster Session Colloquium typically held at the end of each semester.  More specific requirements about the outcomes expected of students are in the Ph.D. Handbook.

Major problems, trends and developments in information science and librarianship. Critical survey of current and classic research findings.

Precursors to, and characteristics of, human information seeking behavior, individual and social, both within and outside of institutional information systems. Relations between such behavior and information systems design and the relevant technologies.

Examines the basic problems of information retrieval (IR) from theoretical and experimental points of view. Develops a basis for the specification of design principles for IR systems.

Measures, models and methods for macro-evaluation of impact of information systems within their environment and for micro-evaluation of performance of system components. Examines the design, conduct, and results associated with experiments.